Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing

Research output: Contribution to journalConference Articlepeer-review

2 Scopus citations


We address the problem of monitoring a set of binary stochastic processes and generating an alert when the number of anomalies among them exceeds a threshold. For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states (normal or anomalous). Based on the received observations, the decision-maker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations. When the decision is to continue, it then decides whether to collect observations at the next time instant or defer it to a later time. If it chooses to collect observations, it further determines the subset of processes to be probed. To devise this three-step sequential decision-making process, we use a Bayesian formulation wherein we learn the posterior probability on the states of the processes. Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning. Via numerical experiments, we demonstrate the superior performance of our algorithm compared to the traditional model-based algorithms.

Original languageEnglish (US)
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2021
Externally publishedYes
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: Dec 7 2021Dec 11 2021


  • Active hypothesis testing
  • actor-critic algorithm
  • anomaly detection
  • change-point detection
  • deep reinforcement learning
  • dynamic decision-making
  • sequential sensing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


Dive into the research topics of 'Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing'. Together they form a unique fingerprint.

Cite this